Demonstrating TabEE: Tabular Embedding Explanations

Roni Copul, Nave Frost, Tova Milo, Kathy Razmadze

Research output: Contribution to journalConference articlepeer-review

Abstract

We present TabEE, Tabular Embedding Explanations, a framework designed to generate explanations for interpreting tabular embedding models. Our framework aims to furnish both local and global explanations for the original data, facilitating the detection of potential flaws in embedding models. TabEE is versatile and compatible with any tabular embedding algorithm, as it adheres to the black box perspective of embedding models. The generated explanations also enable comparisons between multiple embedding models. This demonstration illustrates the effectiveness of TabEE in providing interpretable insights into tabular embedding models, contributing to improved model understanding and credibility assessment.

Original languageEnglish
Pages (from-to)4285-4288
Number of pages4
JournalProceedings of the VLDB Endowment
Volume17
Issue number12
DOIs
StatePublished - 2024
Event50th International Conference on Very Large Data Bases, VLDB 2024 - Guangzhou, China
Duration: 24 Aug 202429 Aug 2024

All Science Journal Classification (ASJC) codes

  • Computer Science (miscellaneous)
  • General Computer Science

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